Huntington National Bank Business Intelligence Interview Guide

Getting ready for a Business Intelligence interview at Huntington National Bank? The Huntington National Bank Business Intelligence interview process typically spans analytical, technical, and strategic question topics, and evaluates skills in areas like data warehousing, reporting, SQL, and communicating actionable insights to stakeholders. Interview prep is particularly important for this role at Huntington National Bank, as candidates are expected to navigate complex financial datasets, design scalable data pipelines, and deliver clear, impactful business intelligence solutions that drive decision-making in a highly regulated and customer-focused environment.

In preparing for the interview, you should:

• Understand the core skills necessary for Business Intelligence positions at Huntington National Bank.
• Gain insights into Huntington National Bank’s Business Intelligence interview structure and process.
• Practice real Huntington National Bank Business Intelligence interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the Huntington National Bank Business Intelligence interview process, along with sample questions and preparation tips tailored to help you succeed.

Huntington National Bank is a leading regional bank headquartered in Columbus, Ohio, serving individuals, small businesses, and commercial clients primarily across the Midwest and Mid-Atlantic regions. The bank offers a comprehensive range of financial services, including personal and business banking, lending, wealth management, and treasury solutions. With a strong commitment to customer service and community involvement, Huntington emphasizes innovation and responsible banking practices. As part of the Business Intelligence team, you will play a crucial role in leveraging data and analytics to drive informed decision-making and support the bank’s mission of helping customers achieve financial success.

As a Business Intelligence professional at Huntington National Bank, you will be responsible for gathering, analyzing, and interpreting data to support strategic decision-making across the organization. You will develop dashboards and reports, collaborate with business units to identify key performance metrics, and translate complex data into actionable insights for leadership. This role often involves working with data warehouses, ensuring data quality, and utilizing tools such as SQL and BI platforms. By providing clear and accurate business intelligence, you help drive operational efficiency, improve customer experience, and support Huntington National Bank’s growth objectives.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough review of your application and resume by the business intelligence recruiting team. They look for demonstrated experience in business analytics, data visualization, ETL pipeline development, data warehousing, and the ability to communicate technical insights to non-technical stakeholders. Emphasis is placed on skills in SQL, Python, dashboarding tools, and experience with financial or large-scale transactional data. To prepare, ensure your resume clearly highlights relevant technical skills, prior business intelligence project outcomes, and your impact on data-driven decision-making.

2.2 Stage 2: Recruiter Screen

A recruiter from Huntington National Bank will conduct an initial phone screen, typically lasting 30 minutes. This conversation focuses on your background, motivations for applying, and alignment with the bank’s values and culture. Expect to discuss your interest in business intelligence, your understanding of the company’s mission, and your general experience with data analytics in the financial sector. Preparation should include a concise narrative about your career path, your reasons for pursuing a BI role at a financial institution, and familiarity with Huntington’s business model.

2.3 Stage 3: Technical/Case/Skills Round

This stage usually consists of one or two rounds led by business intelligence managers or senior data professionals. You’ll be assessed on your technical proficiency in SQL (e.g., writing queries to count transactions or resolve ETL errors), data modeling, and your ability to design or critique data pipelines and warehouses. Case studies may involve real-world scenarios such as analyzing multiple data sources, evaluating A/B tests, or designing dashboards for sales performance and merchant acquisition. You may also be asked to compare tools (e.g., Python vs. SQL), discuss data quality strategies, and extract insights from complex datasets. To prepare, review your hands-on experience with data pipelines, business metrics, and visualization best practices, and practice articulating your analytical approach to ambiguous business problems.

2.4 Stage 4: Behavioral Interview

The behavioral round is often conducted by a hiring manager or a cross-functional team member. Here, you’ll be evaluated on your problem-solving approach, communication skills, and ability to present complex data insights to both technical and non-technical audiences. Expect questions about previous data projects, challenges you faced, and how you made insights accessible for business decision-makers. You’ll also need to demonstrate adaptability, teamwork, and your approach to demystifying data for various stakeholders. Prepare by reflecting on specific examples where your business intelligence work drove impact, and practice explaining technical concepts in simple, actionable terms.

2.5 Stage 5: Final/Onsite Round

The final stage may be a virtual or onsite panel interview with business intelligence leaders, analytics directors, and potential team members. This round often combines a technical deep dive (such as designing a data warehouse for a new product or presenting an end-to-end analytics solution) with scenario-based questions about data-driven decision-making in a financial context. You may be asked to walk through a previous project, present findings to a mock business audience, or troubleshoot a data pipeline in real time. Preparation should focus on your ability to synthesize technical and business perspectives, present insights clearly, and collaborate effectively under pressure.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll engage with the recruiter to discuss compensation, benefits, and start date. This stage may also include conversations with HR to finalize details and answer any remaining questions about the role or company culture. Be prepared to articulate your value, discuss your expectations, and negotiate based on your experience and market standards.

2.7 Average Timeline

The typical Huntington National Bank Business Intelligence interview process spans 3-5 weeks from application to offer. Fast-track candidates with highly relevant experience and strong referrals may progress in as little as 2-3 weeks, while the standard timeline involves approximately one week between each stage, depending on team and candidate availability. Take-home technical assignments or case studies may add a few days to the process, especially if panel scheduling is required.

Next, let’s explore the types of interview questions you’re likely to encounter at each stage of the process.

3.1 Data Analytics & Business Impact

In business intelligence roles, expect questions that assess your ability to translate raw data into actionable insights and measurable business outcomes. Focus on how you would evaluate initiatives, measure success, and communicate findings to stakeholders. Demonstrate your grasp of key business metrics and your approach to experimentation and data-driven decision-making.

3.1.1 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Frame your answer around defining success metrics (e.g., customer acquisition, retention, revenue impact), designing an experiment, and tracking both short- and long-term effects. Discuss how you would communicate results and recommend next steps to leadership.

3.1.2 The role of A/B testing in measuring the success rate of an analytics experiment
Explain the importance of experiment design, control/treatment groups, and statistical significance. Highlight how you set up, monitor, and interpret A/B tests to guide business decisions.

3.1.3 How to model merchant acquisition in a new market?
Describe how you would leverage data to forecast acquisition rates, segment targets, and identify drivers of success. Emphasize predictive modeling and iterative measurement.

3.1.4 Let’s say that you're in charge of an e-commerce D2C business that sells socks. What business health metrics would you care?
List and prioritize key metrics (e.g., conversion rate, retention, average order value), explaining how each informs business strategy. Show your ability to connect analytics to business goals.

3.1.5 Assessing the market potential and then use A/B testing to measure its effectiveness against user behavior
Discuss how you would use market research and controlled experiments to validate new product features, focusing on actionable insights and iterative improvement.

3.2 Data Warehousing & ETL Design

Business intelligence at Huntington National Bank often involves designing robust data systems and pipelines to support reporting and analytics. These questions test your experience with data architecture, ETL processes, and ensuring quality and scalability.

3.2.1 Design a data warehouse for a new online retailer
Outline your approach to schema design, data modeling, and integrating multiple sources. Highlight considerations for scalability, data integrity, and future analytics needs.

3.2.2 Let's say that you're in charge of getting payment data into your internal data warehouse.
Explain how you would design and monitor an ETL pipeline, ensure data quality, and handle errors or late-arriving data. Emphasize automation and reliability.

3.2.3 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Discuss strategies for handling diverse data formats, maintaining consistency, and optimizing performance in large-scale environments.

3.2.4 Ensuring data quality within a complex ETL setup
Describe your approach to data validation, monitoring, and remediation of quality issues. Share examples of tools and processes you’ve implemented.

3.2.5 How would you design a data warehouse for a e-commerce company looking to expand internationally?
Explain how you would address localization, regulatory compliance, and multi-region data integration in your warehouse design.

3.3 Data Engineering & Automation

Expect to discuss building and maintaining data pipelines, automating reporting, and optimizing data flows for business intelligence. These questions probe your technical skills and process improvement mindset.

3.3.1 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Detail your pipeline architecture, choices of tools, and how you ensure reliability and scalability. Highlight the importance of monitoring and error handling.

3.3.2 Design a reporting pipeline for a major tech company using only open-source tools under strict budget constraints.
Show your familiarity with open-source BI and ETL tools, and discuss cost-effective strategies for reporting and dashboarding.

3.3.3 Redesign batch ingestion to real-time streaming for financial transactions.
Explain the challenges and benefits of real-time data processing, including latency, scalability, and data consistency.

3.3.4 Assess and create an aggregation strategy for slow OLAP aggregations.
Discuss methods for optimizing query performance, indexing, and pre-aggregation. Emphasize your approach to balancing speed and resource usage.

3.3.5 Prioritized debt reduction, process improvement, and a focus on maintainability for fintech efficiency
Describe your strategy for identifying and reducing technical debt, and how you prioritize improvements for long-term efficiency.

3.4 Data Quality & Governance

Robust data governance and quality assurance are critical in banking and analytics roles. Expect questions about identifying, diagnosing, and resolving data issues, as well as implementing standards and controls.

3.4.1 How would you approach improving the quality of airline data?
Share your step-by-step approach to profiling, cleaning, and monitoring data. Discuss tools and frameworks for ongoing quality assurance.

3.4.2 Write a query to get the current salary for each employee after an ETL error.
Demonstrate your problem-solving skills in recovering from data integrity issues, using SQL and validation checks.

3.4.3 There was a robbery from the ATM at the bank where you work. Some unauthorized withdrawals were made, and you need to help your bank find out more about those withdrawals.
Explain your approach to investigating anomalies, combining data sources, and presenting findings to support fraud detection.

3.4.4 Write a SQL query to count transactions filtered by several criterias.
Show your proficiency in SQL and your ability to design queries that filter and aggregate data accurately for reporting.

3.4.5 You're analyzing political survey data to understand how to help a particular candidate whose campaign team you are on. What kind of insights could you draw from this dataset?
Discuss techniques for cleaning, merging, and analyzing survey data to extract actionable insights.

3.5 Communication & Data Visualization

Business intelligence analysts must effectively communicate complex insights to diverse audiences. These questions focus on your ability to tailor presentations, visualize data, and translate findings for non-technical stakeholders.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe your approach to audience analysis, visualization selection, and storytelling with data.

3.5.2 Making data-driven insights actionable for those without technical expertise
Explain how you break down technical findings, use analogies, and provide context to drive action.

3.5.3 Demystifying data for non-technical users through visualization and clear communication
Share examples of effective visualizations and communication strategies for non-technical audiences.

3.5.4 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Discuss your choice of visualization techniques and how you summarize or highlight important patterns.

3.5.5 Designing a dynamic sales dashboard to track McDonald's branch performance in real-time
Outline your dashboard design principles, including real-time updates, user experience, and actionable metrics.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Describe a situation where your analysis led to a specific business recommendation or action. Focus on the impact and how you measured success.

3.6.2 Describe a challenging data project and how you handled it.
Share details about the obstacles you faced, your approach to overcoming them, and the outcome. Emphasize resourcefulness and problem-solving.

3.6.3 How do you handle unclear requirements or ambiguity?
Explain your process for clarifying goals, collaborating with stakeholders, and iterating on solutions. Highlight communication and adaptability.

3.6.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Describe how you facilitated open discussion, presented data-driven reasoning, and reached consensus.

3.6.5 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Share strategies for improving communication, such as using visual aids, simplifying language, or scheduling regular check-ins.

3.6.6 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Explain your approach to prioritization, setting boundaries, and maintaining transparency with stakeholders.

3.6.7 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
Discuss how you communicated risks, adjusted deliverables, and provided interim updates to maintain trust.

3.6.8 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe how you built credibility, leveraged data, and navigated organizational dynamics to drive change.

3.6.9 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Explain your prototyping process, how you gathered feedback, and how it led to alignment.

3.6.10 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Describe the tools or scripts you implemented, the impact on efficiency, and how you monitored ongoing data quality.

4.1 Company-specific tips:

Familiarize yourself with Huntington National Bank’s core business lines, including personal banking, commercial lending, and treasury management. Understanding the bank’s regional footprint and customer segments will help you contextualize analytics questions and tailor your responses to real-world banking scenarios.

Research Huntington’s commitment to innovation and responsible banking practices. Be ready to discuss how business intelligence can support both regulatory compliance and customer-centric initiatives, such as improving financial wellness or streamlining loan approvals.

Review recent financial reports, press releases, and product launches from Huntington National Bank. This will help you anticipate business priorities and frame your answers around supporting strategic objectives like digital transformation, fraud detection, or operational efficiency.

Learn about the bank’s technology stack and data infrastructure, focusing on their use of data warehouses, BI platforms, and reporting tools. Understanding how Huntington manages and leverages financial data will allow you to speak confidently about potential improvements or solutions.

4.2 Role-specific tips:

Demonstrate expertise in designing and maintaining scalable data warehouses for financial data.
Be prepared to discuss your experience with data modeling, schema design, and integrating multiple data sources. Highlight how you ensure scalability, data integrity, and compliance with banking regulations, especially when dealing with sensitive customer or transaction data.

Showcase your ability to build robust ETL pipelines and automate reporting processes.
Discuss your approach to developing reliable ETL workflows that ingest, clean, and transform large volumes of transactional data. Emphasize your strategies for error handling, monitoring, and automating recurring tasks to ensure timely and accurate reporting for business stakeholders.

Articulate your process for ensuring data quality and governance in a regulated environment.
Banks require strict data controls—describe how you implement validation checks, monitor data flows, and remediate quality issues. Provide examples of how you’ve addressed data integrity challenges, such as recovering from ETL errors or investigating anomalies in financial transactions.

Demonstrate proficiency in SQL and dashboarding tools for business reporting.
Expect technical questions that require writing queries to aggregate, filter, and analyze banking data. Practice explaining your approach to designing dashboards that track key performance indicators, visualize trends, and support decision-making for leadership.

Highlight your experience communicating actionable insights to both technical and non-technical audiences.
Share examples of how you’ve translated complex analytics into clear recommendations for executives, branch managers, or product teams. Focus on tailoring visualizations, simplifying technical jargon, and storytelling with data to drive business impact.

Prepare to discuss A/B testing and experimentation in a financial context.
Banks often pilot new products or process changes—be ready to explain how you design experiments, measure success, and interpret results. Emphasize your ability to link analytics to customer outcomes, operational efficiency, or revenue growth.

Show your adaptability and teamwork in cross-functional projects.
Reflect on times you collaborated with IT, compliance, or business units to deliver BI solutions. Discuss how you clarified ambiguous requirements, negotiated scope, and aligned stakeholders with differing priorities.

Illustrate your approach to process improvement and technical debt reduction.
Share your strategies for identifying inefficiencies in data pipelines, prioritizing automation, and implementing maintainable solutions. Highlight how your improvements have led to faster reporting, reduced errors, or better scalability for future analytics needs.

Demonstrate your ability to investigate and resolve data anomalies, especially in fraud detection scenarios.
Banks face unique challenges in monitoring for unauthorized activity—describe your approach to anomaly detection, combining multiple data sources, and presenting findings that support risk mitigation.

Be ready to share stories of driving business impact through data-driven recommendations.
Prepare examples where your analysis led to measurable improvements, such as increased customer retention, optimized loan processing, or enhanced sales performance. Focus on how you identified key metrics, communicated findings, and influenced decision-makers.

5.1 “How hard is the Huntington National Bank Business Intelligence interview?”
The Huntington National Bank Business Intelligence interview is moderately challenging, especially for candidates new to the financial sector. You’ll be expected to demonstrate strong technical skills in SQL, data warehousing, ETL pipeline design, and business analytics, as well as a strategic mindset for solving real-world banking problems. The process tests both your technical depth and your ability to translate complex data into actionable insights for business stakeholders. Candidates with experience handling large financial datasets, regulatory compliance, and cross-functional communication will find themselves well-prepared.

5.2 “How many interview rounds does Huntington National Bank have for Business Intelligence?”
Typically, there are five to six rounds in the Huntington National Bank Business Intelligence interview process. These include an initial application and resume review, a recruiter phone screen, one or two technical/case interviews, a behavioral interview, and a final onsite or virtual panel interview. Each round is designed to assess a different aspect of your technical expertise, business acumen, and cultural fit.

5.3 “Does Huntington National Bank ask for take-home assignments for Business Intelligence?”
Yes, it is common for candidates to receive a take-home technical assignment or case study. These assignments often focus on real-world scenarios, such as designing a data pipeline, analyzing financial transactions, or building a dashboard to track key banking metrics. The goal is to assess your practical problem-solving skills, attention to data quality, and ability to communicate insights clearly.

5.4 “What skills are required for the Huntington National Bank Business Intelligence?”
Key skills for this role include advanced SQL, data modeling, ETL pipeline development, and experience with business intelligence tools such as Tableau or Power BI. You should be adept at analyzing large financial datasets, ensuring data quality, and designing scalable data warehouses. Strong communication skills are essential for translating technical findings into actionable recommendations for both technical and non-technical stakeholders. Familiarity with regulatory compliance, data governance, and financial services analytics is a significant advantage.

5.5 “How long does the Huntington National Bank Business Intelligence hiring process take?”
The typical hiring process for Business Intelligence roles at Huntington National Bank takes around three to five weeks from application to offer. Timelines can vary based on candidate availability, scheduling for panel interviews, and the complexity of take-home assignments. Fast-track candidates may progress in as little as two to three weeks, especially if they have highly relevant experience or internal referrals.

5.6 “What types of questions are asked in the Huntington National Bank Business Intelligence interview?”
You can expect technical questions on SQL, data warehousing, ETL pipeline design, and data quality assurance. Case interviews may involve analyzing financial KPIs, designing dashboards, or troubleshooting data anomalies. Behavioral questions focus on communication, teamwork, and your approach to ambiguous requirements. Scenario-based questions may explore your experience with regulatory compliance, fraud detection, or process improvement in a banking context.

5.7 “Does Huntington National Bank give feedback after the Business Intelligence interview?”
Huntington National Bank typically provides feedback through recruiters, especially for candidates who reach the later stages of the process. While detailed technical feedback may be limited, you can expect high-level insights into your strengths and areas for improvement. Candidates are encouraged to request feedback at the end of the process to help guide future preparation.

5.8 “What is the acceptance rate for Huntington National Bank Business Intelligence applicants?”
While specific acceptance rates are not publicly disclosed, the Business Intelligence role at Huntington National Bank is competitive. The acceptance rate is estimated to be in the range of 3-7% for qualified applicants, reflecting the high standards for technical and business acumen in a regulated financial environment.

5.9 “Does Huntington National Bank hire remote Business Intelligence positions?”
Huntington National Bank does offer remote and hybrid options for Business Intelligence roles, depending on team needs and candidate location. Some roles may require occasional visits to regional offices for collaboration or onboarding, but flexible work arrangements are increasingly common, especially for experienced analytics professionals.

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Ready to ace your Huntington National Bank Business Intelligence interview? It’s not just about knowing the technical skills—you need to think like a Huntington National Bank Business Intelligence professional, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Huntington National Bank and similar companies.

With resources like the Huntington National Bank Business Intelligence Interview Guide and our latest case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition.

Take the next step—explore more case study questions, try mock interviews, and browse targeted prep materials on Interview Query. Bookmark this guide or share it with peers prepping for similar roles. It could be the difference between applying and offering. You’ve got this!